1. Build the image dataset

2. Build an image classifier

import numpy as np   
import pandas as pd
import matplotlib.pyplot as plt 
%matplotlib inline 
import seaborn as sns

import os
import cv2
import warnings
import cv2 as cv
from glob import glob
from termcolor import colored
warnings.filterwarnings('ignore')

import itertools

import PIL.Image as Image

from tqdm import tqdm

import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import keras
from keras.utils import np_utils
from tensorflow.keras import layers
from tensorflow.keras.activations import softmax
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import optimizers,regularizers
from tensorflow.python.keras.models import Sequential
from tensorflow.keras.layers import Reshape, Activation
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.layers import Dropout,BatchNormalization, Activation
from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPool2D
from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor



from sklearn import svm
from sklearn.svm import SVC
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report

print(tf.__version__)

2.4.1

from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

print(colored('\x1B[1mImporting data from drive.....','blue'))
cars = '/content/drive/MyDrive/Files/Cars Images'
n_cars = os.listdir(cars)

Importing data from drive.....

print(colored('\x1B[1m Getting details of train dataset.....','blue'))
print('\x1B[1m Total no of images inside test dataset are',len(n_cars))

 Getting details of train dataset.....
 Total no of images inside test dataset are 15

print(colored('\x1B[1mChanging image to array form.....','blue'))
car_path = '/content/drive/MyDrive/Files/Cars Images/*.jpg'
cars = glob(car_path)
car_images = []

for car in cars:
    car_images.append(cv2.resize(cv2.imread(car), (224, 224)))
car_I = np.asarray(car_images)

Changing image to array form.....

print(colored('\x1B[1mVisualizing the images.....','blue'))

Visualizing the images.....

n=0
for x in range(0,15):
  print('\t     Image No.',x+1)
  plt.figure(figsize=(10, 10))
  n=+1
  plt.subplot(2,2, n)
  plt.imshow(car_I[x])
  plt.show()
  print('\n')

	     Image No. 1

	     Image No. 2

	     Image No. 3

	     Image No. 4

	     Image No. 5

	     Image No. 6

	     Image No. 7

	     Image No. 8

	     Image No. 9

	     Image No. 10

	     Image No. 11

	     Image No. 12

	     Image No. 13

	     Image No. 14

	     Image No. 15

car_I.shape

(15, 224, 224, 3)

print(colored('\x1B[1mNormalizing the pixels of images.....','blue'))
car_I = car_I/255

Normalizing the pixels of images.....

X=car_I

Building the image dataset

print(colored('\x1B[1mBuilding image dataset.....','green'))

Building image dataset.....

print(colored('\x1B[1mInitializing Mobilenet calssification model.....','blue'))
model = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2",)])

Initializing Mobilenet calssification model.....

print(colored('\x1B[1mPredicting the images through the model.....','blue'))
results = []
for i in range(0, 15):
  result = model.predict(X[i][np.newaxis, ...])
  results.append(result)

Predicting the images through the model.....

print(colored('\x1B[1mNew shape of the images.....','blue'))
for i in range(0, 15):
  print('Sahpe of image',i+1,'after predicition',results[i].shape)

New shape of the images.....
Sahpe of image 1 after predicition (1, 1001)
Sahpe of image 2 after predicition (1, 1001)
Sahpe of image 3 after predicition (1, 1001)
Sahpe of image 4 after predicition (1, 1001)
Sahpe of image 5 after predicition (1, 1001)
Sahpe of image 6 after predicition (1, 1001)
Sahpe of image 7 after predicition (1, 1001)
Sahpe of image 8 after predicition (1, 1001)
Sahpe of image 9 after predicition (1, 1001)
Sahpe of image 10 after predicition (1, 1001)
Sahpe of image 11 after predicition (1, 1001)
Sahpe of image 12 after predicition (1, 1001)
Sahpe of image 13 after predicition (1, 1001)
Sahpe of image 14 after predicition (1, 1001)
Sahpe of image 15 after predicition (1, 1001)

print(colored('\x1B[1mThe top class ID can be found with argmax:.....','blue'))
predicted_classes = []
for i in range(0, 15):
  predicted_class = np.argmax(results[i][0], axis=-1)
  predicted_classes.append(predicted_class)

The top class ID can be found with argmax:.....

print(colored('\x1B[1mDownloading the Imagenet data for predicting the labels.....','blue'))
labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
imagenet_labels = np.array(open(labels_path).read().splitlines())

Downloading the Imagenet data for predicting the labels.....

print(colored('\x1B[1mPredicting the labels.....','blue'))
labels=[]
for i in range(0, 15):
  plt.imshow(X[i])
  plt.axis('off')
  predicted_class_name = imagenet_labels[predicted_classes[i]]
  _ = plt.title("Prediction: " + predicted_class_name.title())
  labels.append(predicted_class_name)
  plt.show()

Predicting the labels.....

Building an image classifier

print(colored('\x1B[1mBuilding image calssifier.....','green'))

Building image calssifier.....

print(colored('\x1B[1mLabels.....','blue'))
a=labels
a

Labels.....

['barn',
 'beach wagon',
 'minivan',
 'minivan',
 'minivan',
 'minivan',
 'minivan',
 'convertible',
 'sports car',
 'sports car',
 'minivan',
 'pickup',
 'convertible',
 'beach wagon',
 'limousine']

print(colored('\x1B[1mUnique labels are.....','blue'))
len(np.unique(a))

Unique labels are.....

7

Y = LabelEncoder()
Y.fit(a)
print('Classes'+str(Y.classes_))

Classes['barn' 'beach wagon' 'convertible' 'limousine' 'minivan' 'pickup'
 'sports car']

y = Y.transform(a)
classes = y.shape
print(str(classes))

(15,)

print(colored('\x1B[1mLength of total image set','blue'))
lenofimage = len(X)
lenofimage

Length of total image set

15

print(colored('\x1B[1mReshaping of image dataset','blue'))
x = np.array(X).reshape(lenofimage,-1)
print('\x1B[1m',x.shape)

Reshaping of image dataset
 (15, 150528)

print(colored('\x1B[1mChanging target labels from list to array','blue'))
#y=np.array(target_val)
print('\x1B[1m',y.shape)

Changing target labels from list to array
 (15,)

print(colored('\x1B[1mSpliting','blue'))
X_train, X_test, y_train, y_test = train_test_split(x, y, train_size=0.7, test_size=0.3, random_state=42)
score_train={}
score_test={}
accuracy={}
print(colored('\x1B[1mShape of datasets','green'))
print('Shape of X Train',X_train.shape)
print('Shape of Y Train',y_train.shape)
print('Shape of X Test',X_test.shape)
print('Shape of Y Test',y_test.shape)

Spliting
Shape of datasets
Shape of X Train (10, 150528)
Shape of Y Train (10,)
Shape of X Test (5, 150528)
Shape of Y Test (5,)

Model Building

I Supervised Learning

1. SVM

2. Random Forest

3. Decision Tree

II Neural Network

III CNN

I Supervised Learning

1. SVM

print(colored('\x1B[1mSVM','blue'))
svm = SVC(kernel='linear', gamma= 'auto',C=.1)
svm.fit(X_train, y_train)

SVM

SVC(C=0.1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

print(colored('\x1B[1mPredicition','blue'))
y_pred = svm.predict(X_test)

model_train = svm.score(X_train,y_train)
model_test = svm.score(X_test,y_test)
score_train['SVM'] = model_train
score_test['SVM Test Score']=model_test
accuracy['SVM Accuracy'] = metrics.accuracy_score(y_test, y_pred)

Predicition

print(colored('\x1B[1mAccuracy','blue'))
print('Train Score:',model_train)
print('Test Score:',model_test)

Accuracy
Train Score: 1.0
Test Score: 0.2

print(colored('\x1B[1mClassification Report','blue'))
print(classification_report(y_test, y_pred))
cm_svc = (y_test != y_pred).sum()
print('\n Misclassified samples in SVM: {}'.format(cm_svc))

Classification Report
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         1
           1       0.00      0.00      0.00         1
           4       0.20      1.00      0.33         1
           5       0.00      0.00      0.00         1
           6       0.00      0.00      0.00         1

    accuracy                           0.20         5
   macro avg       0.04      0.20      0.07         5
weighted avg       0.04      0.20      0.07         5


 Misclassified samples in SVM: 4

I Supervised Learning

2. Random Forest

print(colored('\x1B[1mRandom Forest','blue'))
rf = RandomForestClassifier(n_estimators= 25, max_features= 6, max_depth= 10, bootstrap= False)

Random Forest

print(colored('\x1B[1mPredicition','blue'))
rf.fit(X_train,y_train)

y_pred = rf.predict(X_test)

model_train = rf.score(X_train,y_train)
model_test = rf.score(X_test,y_test)
score_train['Random Forest'] = model_train
score_test['Random Forest Test Score']=model_test
accuracy['Random Forest Accuracy'] = metrics.accuracy_score(y_test, y_pred)

Predicition

print(colored('\x1B[1mAccuracy','blue'))
print('Train Score:',model_train)
print('Test Score:',model_test)

Accuracy
Train Score: 1.0
Test Score: 0.0

print(colored('\x1B[1mClassification Report','blue'))
print(classification_report(y_test, y_pred))
cm_rf = (y_test != y_pred).sum()
print('\n Misclassified samples in Random Forest: {}'.format(cm_rf))

Classification Report
              precision    recall  f1-score   support

           0       0.00      0.00      0.00       1.0
           1       0.00      0.00      0.00       1.0
           4       0.00      0.00      0.00       1.0
           5       0.00      0.00      0.00       1.0
           6       0.00      0.00      0.00       1.0

    accuracy                           0.00       5.0
   macro avg       0.00      0.00      0.00       5.0
weighted avg       0.00      0.00      0.00       5.0


 Misclassified samples in Random Forest: 5

I Supervised Learning

3. Decision Tree

print(colored('\x1B[1mCDecision Tree','blue'))
dTree = DecisionTreeClassifier(min_samples_leaf=7, max_features=5, max_depth=5, criterion='gini', random_state=1)

CDecision Tree

print(colored('\x1B[1mPredicition','blue'))
dTree.fit(X_train,y_train)

y_pred = dTree.predict(X_test)

model_train = dTree.score(X_train,y_train)
model_test = dTree.score(X_test,y_test)
score_train['Decision Tree'] = model_train
score_test['Decision Tree Test Score']=model_test
accuracy['Decision Tree Accuracy'] = metrics.accuracy_score(y_test, y_pred)

Predicition

print(colored('\x1B[1mAccuracy','blue'))
print('Train Score:',model_train)
print('Test Score:',model_test)

Accuracy
Train Score: 0.5
Test Score: 0.2

print(colored('\x1B[1mClassification Report','blue'))
print(classification_report(y_test, y_pred))
cm_dTree = (y_test != y_pred).sum()
print('\n Misclassified samples in Decision Tree: {}'.format(cm_dTree))

Classification Report
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         1
           1       0.00      0.00      0.00         1
           4       0.20      1.00      0.33         1
           5       0.00      0.00      0.00         1
           6       0.00      0.00      0.00         1

    accuracy                           0.20         5
   macro avg       0.04      0.20      0.07         5
weighted avg       0.04      0.20      0.07         5


 Misclassified samples in Decision Tree: 4

II Neural Network

print(colored('\x1B[1mSetting labels to categorically labels','blue'))
y_c = to_categorical(y, num_classes=7)

Setting labels to categorically labels

print(colored('\x1B[1mSpliting','blue'))
X_train, X_test, y_train, y_test = train_test_split(x, y_c, train_size=0.7, test_size=0.3, random_state=42)

Spliting

print(colored('\x1B[1mShape of datasets','green'))
print('Shape of X Train',X_train.shape)
print('Shape of Y Train',y_train.shape)
print('Shape of X Test',X_test.shape)
print('Shape of Y Test',y_test.shape)

Shape of datasets
Shape of X Train (10, 150528)
Shape of Y Train (10, 7)
Shape of X Test (5, 150528)
Shape of Y Test (5, 7)

input = x.shape[1]
input

150528

print(colored('\x1B[1mNeural Network Model Building','blue'))

Neural Network Model Building

hidden_nodes = 256
output_nodes = 7

model = Sequential()
model.add(Dense(hidden_nodes, input_shape=(input,), activation='relu'))
model.add(Dense(hidden_nodes, activation='relu'))
model.add(Dense(output_nodes, activation='softmax', kernel_regularizer=regularizers.l2(0)))
    
opt = tf.keras.optimizers.Adam(lr=.001)
sgd = optimizers.SGD(lr=.001, decay=1e-6, momentum=0.9)
# Compile model
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    
# Fit the model
model.fit(X_train, y_train, epochs=100, batch_size=200, verbose= 1)
score = model.evaluate(X_test, y_test, verbose=0)
score_train['NN'] = model.evaluate(X_train, y_train, verbose=0)
score_test['NN Test Score']=score
accuracy['NN Accuracy'] = score

Epoch 1/100
1/1 [==============================] - 1s 712ms/step - loss: 2.0955 - accuracy: 0.0000e+00
Epoch 2/100
1/1 [==============================] - 0s 156ms/step - loss: 1.3949 - accuracy: 0.5000
Epoch 3/100
1/1 [==============================] - 0s 162ms/step - loss: 1.3330 - accuracy: 0.4000
Epoch 4/100
1/1 [==============================] - 0s 132ms/step - loss: 1.0419 - accuracy: 0.5000
Epoch 5/100
1/1 [==============================] - 0s 127ms/step - loss: 0.6865 - accuracy: 0.7000
Epoch 6/100
1/1 [==============================] - 0s 128ms/step - loss: 0.4558 - accuracy: 0.9000
Epoch 7/100
1/1 [==============================] - 0s 147ms/step - loss: 0.3033 - accuracy: 0.9000
Epoch 8/100
1/1 [==============================] - 0s 131ms/step - loss: 0.2200 - accuracy: 0.9000
Epoch 9/100
1/1 [==============================] - 0s 128ms/step - loss: 0.0786 - accuracy: 1.0000
Epoch 10/100
1/1 [==============================] - 0s 135ms/step - loss: 0.0574 - accuracy: 1.0000
Epoch 11/100
1/1 [==============================] - 0s 133ms/step - loss: 0.0680 - accuracy: 1.0000
Epoch 12/100
1/1 [==============================] - 0s 146ms/step - loss: 0.0288 - accuracy: 1.0000
Epoch 13/100
1/1 [==============================] - 0s 135ms/step - loss: 0.0119 - accuracy: 1.0000
Epoch 14/100
1/1 [==============================] - 0s 126ms/step - loss: 0.0081 - accuracy: 1.0000
Epoch 15/100
1/1 [==============================] - 0s 136ms/step - loss: 0.0070 - accuracy: 1.0000
Epoch 16/100
1/1 [==============================] - 0s 131ms/step - loss: 0.0065 - accuracy: 1.0000
Epoch 17/100
1/1 [==============================] - 0s 152ms/step - loss: 0.0060 - accuracy: 1.0000
Epoch 18/100
1/1 [==============================] - 0s 142ms/step - loss: 0.0052 - accuracy: 1.0000
Epoch 19/100
1/1 [==============================] - 0s 130ms/step - loss: 0.0043 - accuracy: 1.0000
Epoch 20/100
1/1 [==============================] - 0s 127ms/step - loss: 0.0033 - accuracy: 1.0000
Epoch 21/100
1/1 [==============================] - 0s 133ms/step - loss: 0.0025 - accuracy: 1.0000
Epoch 22/100
1/1 [==============================] - 0s 145ms/step - loss: 0.0018 - accuracy: 1.0000
Epoch 23/100
1/1 [==============================] - 0s 140ms/step - loss: 0.0014 - accuracy: 1.0000
Epoch 24/100
1/1 [==============================] - 0s 127ms/step - loss: 0.0011 - accuracy: 1.0000
Epoch 25/100
1/1 [==============================] - 0s 129ms/step - loss: 9.0680e-04 - accuracy: 1.0000
Epoch 26/100
1/1 [==============================] - 0s 133ms/step - loss: 7.7450e-04 - accuracy: 1.0000
Epoch 27/100
1/1 [==============================] - 0s 129ms/step - loss: 6.8118e-04 - accuracy: 1.0000
Epoch 28/100
1/1 [==============================] - 0s 142ms/step - loss: 6.1470e-04 - accuracy: 1.0000
Epoch 29/100
1/1 [==============================] - 0s 144ms/step - loss: 5.6594e-04 - accuracy: 1.0000
Epoch 30/100
1/1 [==============================] - 0s 124ms/step - loss: 5.2902e-04 - accuracy: 1.0000
Epoch 31/100
1/1 [==============================] - 0s 133ms/step - loss: 4.9987e-04 - accuracy: 1.0000
Epoch 32/100
1/1 [==============================] - 0s 125ms/step - loss: 4.7621e-04 - accuracy: 1.0000
Epoch 33/100
1/1 [==============================] - 0s 137ms/step - loss: 4.5635e-04 - accuracy: 1.0000
Epoch 34/100
1/1 [==============================] - 0s 126ms/step - loss: 4.3924e-04 - accuracy: 1.0000
Epoch 35/100
1/1 [==============================] - 0s 121ms/step - loss: 4.2389e-04 - accuracy: 1.0000
Epoch 36/100
1/1 [==============================] - 0s 127ms/step - loss: 4.0973e-04 - accuracy: 1.0000
Epoch 37/100
1/1 [==============================] - 0s 133ms/step - loss: 3.9637e-04 - accuracy: 1.0000
Epoch 38/100
1/1 [==============================] - 0s 131ms/step - loss: 3.8364e-04 - accuracy: 1.0000
Epoch 39/100
1/1 [==============================] - 0s 123ms/step - loss: 3.7145e-04 - accuracy: 1.0000
Epoch 40/100
1/1 [==============================] - 0s 125ms/step - loss: 3.5967e-04 - accuracy: 1.0000
Epoch 41/100
1/1 [==============================] - 0s 133ms/step - loss: 3.4829e-04 - accuracy: 1.0000
Epoch 42/100
1/1 [==============================] - 0s 131ms/step - loss: 3.3725e-04 - accuracy: 1.0000
Epoch 43/100
1/1 [==============================] - 0s 127ms/step - loss: 3.2661e-04 - accuracy: 1.0000
Epoch 44/100
1/1 [==============================] - 0s 136ms/step - loss: 3.1630e-04 - accuracy: 1.0000
Epoch 45/100
1/1 [==============================] - 0s 131ms/step - loss: 3.0639e-04 - accuracy: 1.0000
Epoch 46/100
1/1 [==============================] - 0s 137ms/step - loss: 2.9688e-04 - accuracy: 1.0000
Epoch 47/100
1/1 [==============================] - 0s 133ms/step - loss: 2.8778e-04 - accuracy: 1.0000
Epoch 48/100
1/1 [==============================] - 0s 137ms/step - loss: 2.7907e-04 - accuracy: 1.0000
Epoch 49/100
1/1 [==============================] - 0s 126ms/step - loss: 2.7081e-04 - accuracy: 1.0000
Epoch 50/100
1/1 [==============================] - 0s 137ms/step - loss: 2.6297e-04 - accuracy: 1.0000
Epoch 51/100
1/1 [==============================] - 0s 131ms/step - loss: 2.5554e-04 - accuracy: 1.0000
Epoch 52/100
1/1 [==============================] - 0s 138ms/step - loss: 2.4846e-04 - accuracy: 1.0000
Epoch 53/100
1/1 [==============================] - 0s 122ms/step - loss: 2.4180e-04 - accuracy: 1.0000
Epoch 54/100
1/1 [==============================] - 0s 124ms/step - loss: 2.3552e-04 - accuracy: 1.0000
Epoch 55/100
1/1 [==============================] - 0s 129ms/step - loss: 2.2959e-04 - accuracy: 1.0000
Epoch 56/100
1/1 [==============================] - 0s 129ms/step - loss: 2.2405e-04 - accuracy: 1.0000
Epoch 57/100
1/1 [==============================] - 0s 131ms/step - loss: 2.1884e-04 - accuracy: 1.0000
Epoch 58/100
1/1 [==============================] - 0s 128ms/step - loss: 2.1394e-04 - accuracy: 1.0000
Epoch 59/100
1/1 [==============================] - 0s 145ms/step - loss: 2.0933e-04 - accuracy: 1.0000
Epoch 60/100
1/1 [==============================] - 0s 127ms/step - loss: 2.0501e-04 - accuracy: 1.0000
Epoch 61/100
1/1 [==============================] - 0s 126ms/step - loss: 2.0095e-04 - accuracy: 1.0000
Epoch 62/100
1/1 [==============================] - 0s 126ms/step - loss: 1.9715e-04 - accuracy: 1.0000
Epoch 63/100
1/1 [==============================] - 0s 127ms/step - loss: 1.9359e-04 - accuracy: 1.0000
Epoch 64/100
1/1 [==============================] - 0s 127ms/step - loss: 1.9024e-04 - accuracy: 1.0000
Epoch 65/100
1/1 [==============================] - 0s 125ms/step - loss: 1.8709e-04 - accuracy: 1.0000
Epoch 66/100
1/1 [==============================] - 0s 127ms/step - loss: 1.8414e-04 - accuracy: 1.0000
Epoch 67/100
1/1 [==============================] - 0s 138ms/step - loss: 1.8137e-04 - accuracy: 1.0000
Epoch 68/100
1/1 [==============================] - 0s 138ms/step - loss: 1.7877e-04 - accuracy: 1.0000
Epoch 69/100
1/1 [==============================] - 0s 125ms/step - loss: 1.7632e-04 - accuracy: 1.0000
Epoch 70/100
1/1 [==============================] - 0s 133ms/step - loss: 1.7402e-04 - accuracy: 1.0000
Epoch 71/100
1/1 [==============================] - 0s 130ms/step - loss: 1.7183e-04 - accuracy: 1.0000
Epoch 72/100
1/1 [==============================] - 0s 126ms/step - loss: 1.6983e-04 - accuracy: 1.0000
Epoch 73/100
1/1 [==============================] - 0s 136ms/step - loss: 1.6790e-04 - accuracy: 1.0000
Epoch 74/100
1/1 [==============================] - 0s 132ms/step - loss: 1.6606e-04 - accuracy: 1.0000
Epoch 75/100
1/1 [==============================] - 0s 127ms/step - loss: 1.6438e-04 - accuracy: 1.0000
Epoch 76/100
1/1 [==============================] - 0s 126ms/step - loss: 1.6274e-04 - accuracy: 1.0000
Epoch 77/100
1/1 [==============================] - 0s 135ms/step - loss: 1.6120e-04 - accuracy: 1.0000
Epoch 78/100
1/1 [==============================] - 0s 125ms/step - loss: 1.5981e-04 - accuracy: 1.0000
Epoch 79/100
1/1 [==============================] - 0s 127ms/step - loss: 1.5841e-04 - accuracy: 1.0000
Epoch 80/100
1/1 [==============================] - 0s 125ms/step - loss: 1.5712e-04 - accuracy: 1.0000
Epoch 81/100
1/1 [==============================] - 0s 126ms/step - loss: 1.5587e-04 - accuracy: 1.0000
Epoch 82/100
1/1 [==============================] - 0s 140ms/step - loss: 1.5470e-04 - accuracy: 1.0000
Epoch 83/100
1/1 [==============================] - 0s 126ms/step - loss: 1.5361e-04 - accuracy: 1.0000
Epoch 84/100
1/1 [==============================] - 0s 126ms/step - loss: 1.5256e-04 - accuracy: 1.0000
Epoch 85/100
1/1 [==============================] - 0s 125ms/step - loss: 1.5153e-04 - accuracy: 1.0000
Epoch 86/100
1/1 [==============================] - 0s 156ms/step - loss: 1.5059e-04 - accuracy: 1.0000
Epoch 87/100
1/1 [==============================] - 0s 129ms/step - loss: 1.4966e-04 - accuracy: 1.0000
Epoch 88/100
1/1 [==============================] - 0s 129ms/step - loss: 1.4881e-04 - accuracy: 1.0000
Epoch 89/100
1/1 [==============================] - 0s 134ms/step - loss: 1.4795e-04 - accuracy: 1.0000
Epoch 90/100
1/1 [==============================] - 0s 128ms/step - loss: 1.4711e-04 - accuracy: 1.0000
Epoch 91/100
1/1 [==============================] - 0s 130ms/step - loss: 1.4636e-04 - accuracy: 1.0000
Epoch 92/100
1/1 [==============================] - 0s 130ms/step - loss: 1.4563e-04 - accuracy: 1.0000
Epoch 93/100
1/1 [==============================] - 0s 128ms/step - loss: 1.4491e-04 - accuracy: 1.0000
Epoch 94/100
1/1 [==============================] - 0s 129ms/step - loss: 1.4422e-04 - accuracy: 1.0000
Epoch 95/100
1/1 [==============================] - 0s 127ms/step - loss: 1.4356e-04 - accuracy: 1.0000
Epoch 96/100
1/1 [==============================] - 0s 130ms/step - loss: 1.4292e-04 - accuracy: 1.0000
Epoch 97/100
1/1 [==============================] - 0s 134ms/step - loss: 1.4230e-04 - accuracy: 1.0000
Epoch 98/100
1/1 [==============================] - 0s 136ms/step - loss: 1.4168e-04 - accuracy: 1.0000
Epoch 99/100
1/1 [==============================] - 0s 128ms/step - loss: 1.4111e-04 - accuracy: 1.0000
Epoch 100/100
1/1 [==============================] - 0s 127ms/step - loss: 1.4055e-04 - accuracy: 1.0000

print(colored('\x1B[1mScore','green'),score)

Score [9.118935585021973, 0.0]

III CNN

print(colored('\x1B[1mSpliting Train and Test sets','blue'))
X_train,X_test,y_train,y_test=train_test_split(X,y_c,test_size=0.3,random_state=7)

Spliting Train and Test sets

print(colored('\x1B[1mShape of datasets','green'))
print('Shape of X Train',X_train.shape)
print('Shape of Y Train',y_train.shape)
print('Shape of X Test',X_test.shape)
print('Shape of Y Test',y_test.shape)

Shape of datasets
Shape of X Train (10, 224, 224, 3)
Shape of Y Train (10, 7)
Shape of X Test (5, 224, 224, 3)
Shape of Y Test (5, 7)

print(colored('\x1B[1mPreprocessing Image and applying augmentation','blue'))

image_generator_train = tf.keras.preprocessing.image.ImageDataGenerator(
        rotation_range=20,width_shift_range=0.2,height_shift_range=0.2,
        shear_range=0.3,zoom_range=0.5,horizontal_flip=True,vertical_flip=True,
        brightness_range=[0.2,0.8],featurewise_center=False,samplewise_center=False,
        featurewise_std_normalization=False,samplewise_std_normalization=False,
        fill_mode = "reflect")
image_generator_train.fit(X_train, augment=True)
augmented_train = image_generator_train.flow(np.array(X_train),np.array(y_train))

image_generator_test = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)
image_generator_test.fit(X_test, augment=True)
augmented_test = image_generator_test.flow(np.array(X_test),np.array(y_test))

print(colored('\x1B[1mFitting Train set in Preprocessing and Augmentation featuers','green'))

Preprocessing Image and applying augmentation
Fitting Train set in Preprocessing and Augmentation featuers

print(colored('\x1B[1mBuilding CNN Model','blue'))

Building CNN Model

# Initialising the CNN model
model = Sequential()

# Add a Convolution layer with 32 kernels of 3X3 shape with activation function ReLU
model.add(Conv2D(32, (3, 3), input_shape = (224, 224, 3), activation = 'relu', padding = 'same'))

# Add a Max Pooling layer of size 2X2
model.add(MaxPool2D(pool_size = (2, 2)))

# Add another Convolution layer with 32 kernels of 3X3 shape with activation function ReLU
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same'))

# Adding another pooling layer
model.add(MaxPool2D(pool_size = (2, 2)))

# Add another Convolution layer with 32 kernels of 3X3 shape with activation function ReLU
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same'))

# Adding another pooling layer
model.add(MaxPool2D(pool_size = (2, 2)))

# Flattening the layer before fully connected layers
model.add(Flatten())

# Adding a fully connected layer with 512 neurons
model.add(Dense(units = 512, activation = 'relu'))

# Adding dropout with probability 0.5
model.add(Dropout(0.5))


# Adding a fully connected layer with 128 neurons
model.add(Dense(units = 128, activation = 'relu'))


# The final output layer with 5 neuron to predict the categorical classifcation
model.add(Dense(units = 7, activation = 'softmax'))

from tensorflow.keras.optimizers import Adam

print(colored('\x1B[1mCompiling Model','blue'))
opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.001, amsgrad=False)
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])

Compiling Model

print(colored('\x1B[1mFiting the Model','blue'))

Fiting the Model

history = model.fit(augmented_train,epochs=5,shuffle=True,validation_data=augmented_test)

Epoch 1/5
1/1 [==============================] - 2s 2s/step - loss: 30.2459 - accuracy: 0.1000 - val_loss: 1.9473 - val_accuracy: 0.0000e+00
Epoch 2/5
1/1 [==============================] - 1s 919ms/step - loss: 298.8923 - accuracy: 0.1000 - val_loss: 1.9427 - val_accuracy: 0.2000
Epoch 3/5
1/1 [==============================] - 1s 943ms/step - loss: 124.0225 - accuracy: 0.2000 - val_loss: 1.9427 - val_accuracy: 0.2000
Epoch 4/5
1/1 [==============================] - 1s 914ms/step - loss: 94.3477 - accuracy: 0.2000 - val_loss: 1.9430 - val_accuracy: 0.0000e+00
Epoch 5/5
1/1 [==============================] - 1s 929ms/step - loss: 27.6467 - accuracy: 0.4000 - val_loss: 1.9431 - val_accuracy: 0.0000e+00

pd.DataFrame(history.history)

	loss	accuracy	val_loss	val_accuracy
0	30.245926	0.1	1.947283	0.0
1	298.892334	0.1	1.942688	0.2
2	124.022522	0.2	1.942725	0.2
3	94.347733	0.2	1.942997	0.0
4	27.646656	0.4	1.943134	0.0

score = model.evaluate(X_test, y_test, verbose=0)
score_train['CNN'] = model.evaluate(X_train, y_train, verbose=0)
score_test['CNN Test Score']=score
accuracy['CNN Accuracy'] = score
print(colored('\x1B[1mScore','green'),score)

Score [1.8442113399505615, 0.6000000238418579]

print('''\n\033[1m''' + '''Displaying and comparing all the models designed with their train and test accuracies''' + '''\033[0m''')
results = pd.DataFrame({'Model' : score_train.keys(),
                        'Train Score' : score_train.values(),
                        'Train Score': score_test.values(),
                        'Accuracy' : accuracy.values()})
results

Displaying and comparing all the models designed with their train and test accuracies

	Model	Train Score	Accuracy
0	SVM	0.2	0.2
1	Random Forest	0	0
2	Decision Tree	0.2	0.2
3	NN	[9.118935585021973, 0.0]	[9.118935585021973, 0.0]
4	CNN	[1.8442113399505615, 0.6000000238418579]	[1.8442113399505615, 0.6000000238418579]

print(colored('\x1B[1m\t     Loss analysis graph of Model\n','green'))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'test'])
plt.show()

	     Loss analysis graph of Model

print(colored('\x1B[1m\t  Accuracy analysis graph of Model\n','green'))
plt.plot(np.array(history.history['accuracy']) * 100)
plt.plot(np.array(history.history['val_accuracy']) * 100)
plt.ylabel('accuracy')
plt.xlabel('epochs')
plt.legend(['train', 'validation'])
plt.title('Accuracy over epochs')
plt.show()

	  Accuracy analysis graph of Model

def plot_confusion_matrix(cm, classes, normalize=False,title='Confusion matrix',cmap=plt.cm.Greens):
    
    fig = plt.figure(figsize=(10,10))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('Actual label')
    plt.xlabel('Predicted label')

print(colored('\x1B[1mPredict the values from the test data','blue'))
y_pred = model.predict(X_test)
y_pred_Classes = np.argmax(y_pred, axis = 1) 
trueY = np.argmax(y_test, axis = 1) 

Predict the values from the test data

print(colored('\x1B[1mConfusion Matrix of Model','blue'))
confusionMTX = confusion_matrix(trueY, y_pred_Classes) 
plot_confusion_matrix(confusionMTX, classes = Y.classes_) 

Confusion Matrix of Model

Though the accuracy of all model comes to be very poor.

But it is not possible to build image classifier without labels.

To build image clssifier, labels are needed.

To label the images respectively, image dataset shuold be build, which can be only done through using a pretrained classifer model and its data.